African Clinical Nutrition | 07 November 2011
Methodological Evaluation of Public Health Surveillance Systems in Nigeria Using Time-Series Forecasting Models for Risk Reduction Measurement
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Abstract
Public health surveillance systems in Nigeria are crucial for monitoring disease prevalence and guiding intervention strategies. Time-series forecasting models will be employed to analyse surveillance data from Nigeria. The Box-Jenkins model (ARIMA) will be used for its robustness and ability to handle temporal dependencies. The analysis revealed a significant positive trend in disease incidence over the past decade, with a forecasted reduction of approximately 20% by under current intervention strategies. This study highlights the effectiveness of time-series forecasting models for monitoring public health trends and could inform future policy development. Future surveillance systems should incorporate early warning signals to facilitate timely interventions, based on the findings from this review. Treatment effect was estimated with $\text{logit}(p<em>i)=\beta</em>0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.